A Comprehensive Comparison of MobileNet, ResNet50 and InceptionV3 for Efficient Plant Pathology Detection
- Title
- A Comprehensive Comparison of MobileNet, ResNet50 and InceptionV3 for Efficient Plant Pathology Detection
- Creator
- Jevoor, Susmita; Girish, V.; Chandan, N.; Umamaheswari, D.; Gondkar, Raju Ramakrishna
- Description
- Plant diseases have a strong impact on agricultural productivity due to economic factors and a reduction in crop quality. This work focuses on the classification of apple leaves into four classes: healthy, rust-infected, scab-infected, and infected by both diseases, using the Plant Pathology FGVC7-2020 dataset that contains 3,642 images in total. The work involves the analysis of three sophisticated deep learning architectures: ResNet50, MobileNet and InceptionV3.It turned out that MobileN et showed the highest performance, reaching a 92% accuracy rate; it was followed by ResNet50 with 75% accuracy and InceptionV3 at 73%, hence underlining its better generalization capability and efficiency in classifying. We discuss the proposed methodology, which includes data preprocessing techniques, experimental results and final conclusions, is discussed in detail. These results underline the fundamental importance of determining an appropriate neural network architecture for the recognition of plant diseases, which is of prime importance to improve agricultural productivity. 2025 IEEE.
- Source
- Proceedings of 2025 International Conference on Computing for Sustainability and Intelligent Future, COMP-SIF 2025;
- Date
- 01-01-2025
- Publisher
- Institute of Electrical and Electronics Engineers Inc.
- Subject
- Agriculture; CNN; Deep learning; Inception V3; MobileNet; Plant disease detection; ResNet50
- Coverage
- Jevoor S., Christ (Deemed to be University), Dept. Computer Science, Bangalore, India; Girish V., Christ (Deemed to be University), Dept. Computer Science, Bangalore, India; Chandan N., Christ (Deemed to be University), Dept. Computer Science, Bangalore, India; Umamaheswari D., Christ (Deemed to be University), Dept. Computer Science, Bangalore, India; Gondkar R.R., Christ (Deemed to be University), Dept. Computer Science, Bangalore, India
- Rights
- Restricted Access; Hardcopy may be available in the library
- Relation
- ISBN: 979-833153853-8;
- Format
- online
- Language
- English
- Type
- Conference paper
Collection
Citation
Jevoor, Susmita; Girish, V.; Chandan, N.; Umamaheswari, D.; Gondkar, Raju Ramakrishna, “A Comprehensive Comparison of MobileNet, ResNet50 and InceptionV3 for Efficient Plant Pathology Detection,” CHRIST (Deemed To Be University) Institutional Repository, accessed June 19, 2026, https://archives.christuniversity.in/items/show/25796.